example_for_mat_file.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4d4139d7",
   "metadata": {},
   "source": [
    "# Example on how to use the python class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d8b67a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Get list of files and labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b4acb30",
   "metadata": {},
   "source": [
    "List files, which are named with 'Traj' in folder 'data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3f6ca77-8450-44f3-9784-015e10be73ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "files = !ls -lh <file_path>/*.mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd50b7da",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [a.split('ory_')[-1].split('_2022')[0] for a in files]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f56681aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BRU_SYD', 'LHR_JFK']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69e21798",
   "metadata": {},
   "source": [
    "## Load class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "398b831e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from package import calculate_rf as rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b03201c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "traj = rf.EmissionInventory(files[0].split()[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d93d1acc",
   "metadata": {},
   "source": [
    "## Calculate radiative forcing and emissions for each trajectory-file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9c2d86c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "net_rf = []\n",
    "net_emis = []\n",
    "\n",
    "for file in files:\n",
    "    trajectory = rf.EmissionInventory(file.split()[-1])\n",
    "    trajectory.drop_vertical_levels()\n",
    "    \n",
    "    net_rf.append(trajectory.total_rf())\n",
    "    \n",
    "    net_emis.append(trajectory.total_emis())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18044456",
   "metadata": {},
   "source": [
    "`calculate_rf(file.split()[-1])` creates a class instance with the trajectory data file.\n",
    "`load_mat_as_dataframe()` extracts the relevant data from the file within `__init__()`.\n",
    "`drop_vertical_levels()` limits the vertical emission to emission above the tropopause, which is the default. Other vertical limits can be given in hectopascal with e.g. `drop_vertical_levels(100)` to limit emissions to above 100 hPa.\n",
    "`total_rf()` returns the stratospheric adjusted radiative forcing of the trajectory.\n",
    "`total_emis()` returns the accumulated emissions for the chosen vertical levels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43f97c9e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fc93ad97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Altitude [km]</th>\n",
       "      <th>tp_WMO [Pa]</th>\n",
       "      <th>Altitude [Pa]</th>\n",
       "      <th>H2O [kg]</th>\n",
       "      <th>NO [kg]</th>\n",
       "      <th>H2 [kg]</th>\n",
       "      <th>H2O RF from H2O [mW m-2]</th>\n",
       "      <th>O3 RF from H2O [mW m-2]</th>\n",
       "      <th>O3 RF from H2 [mW m-2]</th>\n",
       "      <th>O3 RF from NO [mW m-2]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>51.302952</td>\n",
       "      <td>-2.827075</td>\n",
       "      <td>10.950761</td>\n",
       "      <td>23006.607422</td>\n",
       "      <td>22876.031608</td>\n",
       "      <td>1217.678955</td>\n",
       "      <td>0.467333</td>\n",
       "      <td>14.270504</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Latitude  Longitude  Altitude [km]   tp_WMO [Pa]  Altitude [Pa]   \n",
       "179  51.302952  -2.827075      10.950761  23006.607422   22876.031608  \\\n",
       "\n",
       "        H2O [kg]   NO [kg]    H2 [kg] H2O RF from H2O [mW m-2]   \n",
       "179  1217.678955  0.467333  14.270504                 0.000001  \\\n",
       "\n",
       "    O3 RF from H2O [mW m-2] O3 RF from H2 [mW m-2] O3 RF from NO [mW m-2]  \n",
       "179                    -0.0                   -0.0                    0.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trajectory.data.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4257665f",
   "metadata": {},
   "source": [
    "total_emis() returns three values with H$_2$O, H$_2$ and NO emission above the tropopause in tons in this order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3839c58a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1095.47, 3.46, 8.65], [556.47, 4.09, 1.87]]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net_emis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af093de9",
   "metadata": {},
   "source": [
    "total_rf() returns one value with radiative forcing from water vapour changes due to water vapour emisson and ozone radiative forcing from H$_2$O, H$_2$ and NO emission in mWm-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b71b1fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.002277626952656135, 0.0008800361503643599]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net_rf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "893d3737",
   "metadata": {},
   "source": [
    "## Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "765cb478",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(0, 36, 20)\n",
    "bin_idxs = np.digitize(traj.data['Altitude [km]'], bins)\n",
    "data = traj.data.groupby(bin_idxs).sum()\n",
    "data.index=bins[data.index]\n",
    "\n",
    "fig, axs = plt.subplots(1,3, figsize=(14,3), sharey=True)\n",
    "\n",
    "axs[0].barh(data.index,data['H2O [kg]'],height=3.5), axs[0].set_ylabel('Altitude [km]'), axs[0].set_xlabel('H$_2$O [kg]')\n",
    "axs[1].barh(data.index,data['NO [kg]'],height=3.5), axs[1].set_xlabel('NO [kg]')\n",
    "axs[2].barh(data.index,data['H2 [kg]'],height=3.5), axs[2].set_xlabel('H$_2$ [kg]')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5511cbdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,3))\n",
    "\n",
    "columns = ['O3 RF from H2O [mW m-2]','O3 RF from H2 [mW m-2]','O3 RF from NO [mW m-2]','H2O RF from H2O [mW m-2]']\n",
    "\n",
    "data = trajectory.data[columns].sum()\n",
    "data.plot.bar()\n",
    "\n",
    "plt.ylabel('Radiative forcing [mW$\\,$m-2]')\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "1 Python 3 (based on the module python3/2023.01)",
   "language": "python",
   "name": "python3_2023_01"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}